icloud-ecnu / Opara
Opara is a lightweight and resource-aware DNN Operator parallel scheduling framework to accelerate the execution of DNN inference on GPUs.
☆20Updated 4 months ago
Alternatives and similar repositories for Opara:
Users that are interested in Opara are comparing it to the libraries listed below
- spotDNN is a heterogeneity-aware spot instance provisioning framework to provide predictable performance for DDNN training workloads in t…☆15Updated last year
- iSpot is a lightweight and cost-effective instance provisioning framework for Directed Acyclic Graph (DAG)-style big data analytics, in …☆11Updated last year
- ebrowser, an energy-efficient and lightweight human interaction framework without degrading the user experience in mobile Web browsers.☆12Updated last year
- Reading paper list for iCloud group☆13Updated last month
- ☆8Updated 3 years ago
- DelayStage is a simple yet effective stage delay scheduling strategy to interleave the cluster resources across the parallel stages, so a…☆14Updated last year
- Prophet is a predictable communication scheduling strategy to schedule the gradient transfer in an adequate order, with the aim of maximi…☆16Updated last year
- ☆12Updated last year
- iGniter, an interference-aware GPU resource provisioning framework for achieving predictable performance of DNN inference in the cloud.☆37Updated 10 months ago
- λDNN is a cost-efficient function resource provisioning framework to minimize the monetary cost and guarantee the performance for DDNN tr…☆23Updated last year
- Tetris, a model predictive control (MPC)-based container scheduling strategy to judiciously make migration decisions for long-running con…☆24Updated 4 months ago
- Proteus: A High-Throughput Inference-Serving System with Accuracy Scaling☆11Updated last year
- This repository is established to store personal notes and annotated papers during daily research.☆120Updated 2 weeks ago
- ☆48Updated 4 months ago
- HeliosArtifact☆20Updated 2 years ago
- ☆37Updated 3 years ago
- Lucid: A Non-Intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs☆53Updated last year
- Compiler for Dynamic Neural Networks☆46Updated last year
- Artifacts for our ASPLOS'23 paper ElasticFlow☆51Updated 11 months ago
- A ChatGPT(GPT-3.5) & GPT-4 Workload Trace to Optimize LLM Serving Systems☆164Updated 6 months ago
- ☆23Updated 2 years ago
- SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training☆32Updated 2 years ago
- Artifact for USENIX ATC'23: TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs.☆47Updated last year
- ☆31Updated 2 years ago
- ☆9Updated last year
- Artifact for PPoPP22 QGTC: Accelerating Quantized GNN via GPU Tensor Core.☆27Updated 3 years ago
- ☆10Updated 3 months ago
- ☆49Updated 2 years ago
- Supplemental materials for The ASPLOS 2025 / EuroSys 2025 Contest on Intra-Operator Parallelism for Distributed Deep Learning☆23Updated 4 months ago
- Paella: Low-latency Model Serving with Virtualized GPU Scheduling☆58Updated last year